AIMC Topic: Pharmacovigilance

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Learning to detect and understand drug discontinuation events from clinical narratives.

Journal of the American Medical Informatics Association : JAMIA Oct 1, 2019
OBJECTIVE: Identifying drug discontinuation (DDC) events and understanding their reasons are important for medication management and drug safety surveillance. Structured data resources are often incomplete and lack reason information. In this article...
Machine Learning Electronic Health Records Support Vector Machine Humans Pharmacovigilance Product Surveillance, Postmarketing Drug Therapy Narration Natural Language Processing Area Under Curve
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Prediction of Personal Experience Tweets of Medication Use via Contextual Word Representations.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2019
Continuous monitoring the safe use of medication is an important task in pharmacovigilance. The first-hand experiences of medication effects come from the consumers of the pharmaceuticals. Social media have been considered as a possible alternative d...
Electric Power Supplies Neural Networks, Computer Social Media Natural Language Processing Pharmacovigilance
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Comment on: "Deep learning for pharmacovigilance: recurrent neural network architectures for labeling adverse drug reactions in Twitter posts".

Journal of the American Medical Informatics Association : JAMIA Jun 1, 2019
Humans Neural Networks, Computer Drug-Related Side Effects and Adverse Reactions Data Mining Pharmacovigilance Deep Learning Social Media Natural Language Processing Datasets as Topic
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Reply to comment on: "Deep learning for pharmacovigilance: recurrent neural network architectures for labeling adverse drug reactions in Twitter posts".

Journal of the American Medical Informatics Association : JAMIA Jun 1, 2019
We appreciate the detailed review provided by Magge et al1 of our article, "Deep learning for pharmacovigilance: recurrent neural network architectures for labeling adverse drug reactions in Twitter posts." 2 In their letter, they present a subjectiv...
Drug-Related Side Effects and Adverse Reactions Neural Networks, Computer Humans Deep Learning Pharmacovigilance Social Media
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Artificial Intelligence Within Pharmacovigilance: A Means to Identify Cognitive Services and the Framework for Their Validation.

Pharmaceutical medicine Apr 1, 2019
INTRODUCTION: Pharmacovigilance (PV) detects, assesses, and prevents adverse events (AEs) and other drug-related problems by collecting, evaluating, and acting upon AEs. The volume of individual case safety reports (ICSRs) increases yearly, but it is...
Pharmacovigilance Adverse Drug Reaction Reporting Systems Workload Drug-Related Side Effects and Adverse Reactions Algorithms Patient Safety Databases, Factual Guideline Adherence Cognition Decision Making Humans Artificial Intelligence Machine Learning
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Artificial Intelligence and the Future of the Drug Safety Professional.

Drug safety Apr 1, 2019
The healthcare industry, and specifically the pharmacovigilance industry, recognizes the need to support the increasing amount of data received from individual case safety reports (ICSRs). To cope with this increase, more healthcare and qualified pro...
Pharmacovigilance Risk Management Drug-Related Side Effects and Adverse Reactions Artificial Intelligence Machine Learning Algorithms Health Personnel Decision Making Humans
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Data and systems for medication-related text classification and concept normalization from Twitter: insights from the Social Media Mining for Health (SMM4H)-2017 shared task.

Journal of the American Medical Informatics Association : JAMIA Oct 1, 2018
OBJECTIVE: We executed the Social Media Mining for Health (SMM4H) 2017 shared tasks to enable the community-driven development and large-scale evaluation of automatic text processing methods for the classification and normalization of health-related ...
Data Mining Humans Pharmacovigilance Neural Networks, Computer Natural Language Processing Support Vector Machine Drug-Related Side Effects and Adverse Reactions Social Media
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A chronological pharmacovigilance network analytics approach for predicting adverse drug events.

Journal of the American Medical Informatics Association : JAMIA Oct 1, 2018
OBJECTIVES: This study extends prior research by combining a chronological pharmacovigilance network approach with machine-learning (ML) techniques to predict adverse drug events (ADEs) based on the drugs' similarities in terms of the proteins they t...
Computer Communication Networks Datasets as Topic Adverse Drug Reaction Reporting Systems Humans Machine Learning Algorithms Drug Interactions Models, Theoretical Drug-Related Side Effects and Adverse Reactions Pharmacovigilance
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Learning predictive models of drug side-effect relationships from distributed representations of literature-derived semantic predications.

Journal of the American Medical Informatics Association : JAMIA Oct 1, 2018
OBJECTIVE: The aim of this work is to leverage relational information extracted from biomedical literature using a novel synthesis of unsupervised pretraining, representational composition, and supervised machine learning for drug safety monitoring.
Supervised Machine Learning Drug-Related Side Effects and Adverse Reactions Data Mining Pharmacovigilance Semantics Humans Natural Language Processing Product Surveillance, Postmarketing Databases, Bibliographic
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Drug-drug interaction extraction via hierarchical RNNs on sequence and shortest dependency paths.

Bioinformatics (Oxford, England) Mar 1, 2018
MOTIVATION: Adverse events resulting from drug-drug interactions (DDI) pose a serious health issue. The ability to automatically extract DDIs described in the biomedical literature could further efforts for ongoing pharmacovigilance. Most of neural n...
Pharmacovigilance Publications Drug Interactions Data Mining Humans Neural Networks, Computer
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